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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) SITEKIN: Jurnal Sains, Teknologi dan Industri KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Applied Information System and Management ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Economic, Management, Accounting and Technology (JEMATech) KOMPUTIKA - Jurnal Sistem Komputer Jambura Journal of Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) METIK JURNAL Building of Informatics, Technology and Science Dinasti International Journal of Education Management and Social Science Jurnal Tecnoscienza Jurnal Mnemonic Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics PRAJA: Jurnal Ilmiah Pemerintahan JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JIKA (Jurnal Informatika) Jurnal Perangkat Lunak Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Electrical Engineering and Computer (JEECOM) JINAV: Journal of Information and Visualization International Journal of Artificial Intelligence and Robotics (IJAIR) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) DEVICE Djtechno: Jurnal Teknologi Informasi JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer JURNAL STUDIA KOMUNIKA KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Journal Computer Science and Informatic Systems : J-Cosys Jurnal Mandiri IT Sulawesi Tenggara Educational Journal JURNAL PAI: Jurnal Kajian Pendidikan Agama Islam Jurnal Sisfotek Global International Journal Artificial Intelligent and Informatics Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Innovation Research and Knowledge Malcom: Indonesian Journal of Machine Learning and Computer Science Nusantara of Engineering (NOE) Jurnal Bangkit Indonesia Jurnal Multidisiplin Sahombu COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi JEC (Jurnal Edukasi Cendekia) Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Sistem Informasi Komputer dan Teknologi Informasi Jurnal TAM (Technology Acceptance Model) Jurnal Sistem Informasi dan Teknologi Informasi
Kusrini, Kusrini
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Journal : Jambura Journal of Informatics

 1 
Prediksi Kebakaran Hutan Ibu Kota Nusantara Menggunakan Produk MODIS dengan Algoritma Regresi Linear, Gradient Boosting dan Decision Tree Mangun, Syamsul Syahab; Kusrini, Kusrini
Jambura Journal of Informatics VOL 7, N0 1: APRIL 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jji.v1i1.30926

Abstract

Forest Fires in the Capital City of the Archipelago (IKN) threaten environmental sustainability and sustainable development, but accurate predictions are still hampered by the complexity of trigger factors such as weather variability and data uncertainty, so this study aims to develop a machine learning-based forest fire prediction model by utilising MODIS (Moderate Resolution Imaging Spectroradiometer) data, including surface temperature and thermal anomalies, by comparing three algorithms namely Linear Regression, Gradient Boosting Regressor (GBR), and Decision Tree Regressor (DTR). Performance evaluation using RMSE (Root Mean Square Error) and accuracy metrics showed GBR as the best model with 98.84% accuracy followed by DTR 96.39%, while R² values close to 1.0 in both models indicated the ability to explain data variability optimally, in contrast to linear regression which showed significant limitations. Thus, these findings prove the superiority of ensemble algorithms such as GBR in handling non-linearity of forest fire data and have practical implications on its potential integration into early warning systems to improve the effectiveness of  fire mitigation around the IKN region.Kebakaran Hutan di Ibu Kota Nusantara (IKN) mengancam kelestarian lingkungan dan pembangunan berkelanjutan, namun prediksi akurat masih terhambat oleh kompleksitas faktor pemicu seperti variabilitas cuaca dan ketidakpastian data, sehingga penelitian ini bertujuan mengembangkan model prediksi kebakaran hutan berbasis machine learning dengan memanfaatkan data MODIS (Moderate Resolution Imaging Spectroradiometer), termasuk suhu permukaan dan anomali termal, dengan membandingkan tiga algoritma yaitu Regresi Linear, Gradient Boosting Regressor (GBR), dan Decision Tree Regressor (DTR). Evaluasi performa menggunakan metrik RMSE (Root Mean Square Error) dan akurasi yang menunjukan GBR sebagi model terbaik dengan akurasi 98,84% diikuti DTR 96,39%, sementara  nilai R² mendekati 1.0 pada kedua model mengindikasikan kemampuan menjelaskan variabilitas data secara optimal, berbeda dengan regresi linear yang menunjukkan keterbatasan signifikan, sehingga temuan ini membuktikan keunggulan algoritma seperti GBR dalam menangani non-linearitas data kebakaran hutan dan berimplikasi praktis pada potensi integrasinya ke dalam sistem peringatan dini untuk meningkatkan efektivitas mitigasi kebakaran di sekitar wilayah IKN.
Analisis Sentimen Omnibus Law di Twitter dengan Machine Learning dan Teknik Resampling Syafutra, Arif Dwi; Kusrini, Kusrini
Jambura Journal of Informatics VOL 7, N0 1: APRIL 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jji.v1i1.30935

Abstract

The Omnibus Law has been controversial in Indonesia since its enactment in 2020, sparking widespread public debate on social media platforms, particularly Twitter. This study aims to classify public sentiment toward the Omnibus Law using machine learning algorithms and resampling techniques to address data imbalance. Twenty thousand tweets were collected via web scraping, processed using Natural Language Processing (NLP) methods, and automatically labeled through a lexicon-based approach. The final dataset consisted of 17,184 tweets categorized into positive and negative sentiments. Sentiment classification models were developed using Support Vector Machine (SVM), Random Forest, and Multinomial Naïve Bayes, with Synthetic Minority Oversampling Technique (SMOTE) and Random Undersampling applied to enhance model performance. Evaluation results show that SVM combined with SMOTE achieved the highest performance with an accuracy of 93.08%, a recall of 92.85%, and a precision of 92.44%, outperforming other algorithms. These findings highlight that resampling techniques, particularly SMOTE, significantly improve classification performance in handling imbalanced datasets. This study emphasizes the importance of selecting appropriate algorithms and balancing strategies to enhance sentiment analysis accuracy based on social media data. Furthermore, the results open opportunities for future research to explore deep learning-based approaches for more complex public opinion analysis.Omnibus Law telah menjadi isu kontroversial di Indonesia sejak pengesahannya pada tahun 2020 yang mendorong perdebatan luas di media sosial, khususnya Twitter. Penelitian ini bertujuan untuk mengklasifikasikan sentimen publik terhadap Omnibus Law menggunakan algoritma machine learning dan teknik resampling untuk mengatasi ketidakseimbangan data. Data sebanyak 20.000 tweet dikumpulkan melalui web scraping, diproses dengan metode Natural Language Processing (NLP), dan dilabeli secara otomatis menggunakan pendekatan berbasis lexicon. Dataset akhir terdiri atas 17.184 tweet dengan kategori sentimen positif dan negatif. Model klasifikasi dikembangkan menggunakan Support Vector Machine (SVM), Random Forest, dan Multinomial Naïve Bayes, dengan penerapan teknik Synthetic Minority Oversampling Technique (SMOTE) dan Random Undersampling untuk meningkatkan performa. Hasil evaluasi menunjukkan bahwa SVM dengan SMOTE menghasilkan akurasi tertinggi sebesar 93,08%, recall 92,85%, dan precision 92,44%, mengungguli algoritma lainnya. Temuan ini menunjukkan bahwa teknik resampling, khususnya SMOTE, secara signifikan memperbaiki performa klasifikasi dalam skenario data tidak seimbang. Penelitian ini menegaskan pentingnya kombinasi antara pemilihan algoritma yang tepat dan strategi balancing data untuk meningkatkan akurasi analisis sentimen berbasis media sosial. Studi ini juga membuka peluang penelitian lanjutan menggunakan pendekatan deep learning untuk klasifikasi opini publik yang lebih kompleks.
Co-Authors AA Sudharmawan, AA Abdillah, Yahya Auliya Adhani, Muhammad Azmi Agastya, I Made Artha Ahmad Yusuf Alfatta, Hanif Alva Hendi Muhammad Andi Muhammad Irfan Andika, Roy Andriyanto, Rifki Angga Kurniawan Anggraeni, Meita Dwi Ardana, Wildan Muhammmad Ari Yuana, Kumara Arief Setyanto Arief, M Rudyanto Arief, Muhammad Rudyanto Arifuddin, Danang Aris Subadi Asnawi, Muhamad Fuat Azi, Amanda Aziz, Moh Abdul Bayu Setiaji Béjar, Rodrigo Martínez Bentar Candra P Bernadhed, Bernadhed Bisono, Hadi Hikmadyo Braeken, An Candra, Kurnia Khoirul da Silva, Bruno DHANI ARIATMANTO Dzulhijjah, Dwi Ahmad Eko Pramono Eko Purwanto Ema Utami Emha Taufiq Luthfi Fatkhurrochman, Fatkhurrochman Fauzi, Moch Farid Fauzy, Marwan Noor Febrianti, Winda Ferry Wahyu Wibowo fitriyanto, nur Gifari, Okta Ihza Halimi, Ahmad Hamdikatama, Bimantyoso Hanif Al Fatta Haris, Ruby Hartono, Anggit Dwi Haryo, Wasis Hasan, Nur Fitrianingsih Hasan, Nurul Rahmawati Herawati, Maimi Herlinawati, Noor Hulvi, Alfajri I Putu Agus Ari Mahendra Ilmawati, Fahma Inti Jeki Kuswanto Juwariyah, Siti Kasman, Haris Saktiawan Kurniasari, Iin Kusnawi , Kusnawi Kusnawi Kusnawi Lewu, Retzi Y. Listyanto, Ahmad Wildan López, Alba Puelles Lukman Bachtiar M. RUDYANTO ARIEF M. Suyanto, M. Madhika, Yudha Randa Mahendra, Awanda Putra Mangun, Syamsul Syahab Maradona, Maradona Mardiana Mardiana Martínez-Béjar, Rodrigo maulana, fahrizal Megantara, Muhamad Arldi MEI PARWANTO KURNIAWAN Metha, Halifa Sekar Mohamad Firdaus, Mohamad Mohammad Diqi Mohammad Rezza Pahlevi Moningka, Nirwan Mufti Ari Bianto Muhammad Resa Arif Yudianto Muktafin, Elik Hari Muzakir, Muhammad MZ, Reza Rafiq Nasiri, Asro Ni Nyoman Utami Januhari, Ni Nyoman Nugroho, Agung Nugroho, Hanantyo Sri Oktafiqurahman, Andi Olajuwon, Sayyid Muh. Raziq Onde, Mitrakasih La ode Oscar Samaratungga Pamoengkas, Muhamad Agoeng Pamungkas, Sapto Pradipta, Dody Prameswari, Sonia Anjani Prasetio, Agung Budi Prastyo, Rahmat Pratama, Muhammad Egy Puri, Fiyas Mahananing Putra, Andriyan Dwi Rachmawati Oktaria Mardiyanto Riduan, Nor Rizkayati, Anisa S, Muhamad Rois S, Muhammad Sabri Saleh, Robby Febrianur Samponu, Yohakim Benedictus Santosa, Hendriansyah Saputro, Moh. Rizal Bayu Sarawan, Tommy Selvy Megira, Selvy Semma, Andi Bahtiar Setiawan, Moh. Arif Ma'ruf Setyanto, Arif Solikin, Arif Fajar Sudarmawan, Sudarmawan Sudarto Sudarto Swastikawati, Claudia Syafutra, Arif Dwi Syaiful Huda Tampubolon, Jandri Tamuntuan, Virginia TONNY HIDAYAT Tri Nugroho, Arief triadin, Yusrinnatul Jinana Tukan, Ewaldus Ambrosius Ula, M. Izul Wahyu Pujiharto, Eka Wahyudi, Alfian Cahyo Wiwi Widayani, Wiwi Yossy Ariyanto Yuana, Kumara Ari Yuza, Adela Zakaria Zakaria Zuhri, Muhammad Rafli